Nemotron Nano 9B v2 needs ~9.7 GB but GTX 1660 Super 6GB only has 6.0 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
3.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
9.1 tok/s
TTFT
21213 ms
Safe context
4K
Memory
9.7 GB / 6.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 9.7 GB, but this setup only exposes 6.0 GB of usable VRAM.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 12.2 tok/s | 8621 ms | 4K |
| Coding | F | Too heavy | 9.1 tok/s | 21213 ms | 4K |
| Agentic Coding | F | Too heavy | 5.6 tok/s | 50445 ms | 4K |
| Reasoning | F | Too heavy | 9.1 tok/s | 25070 ms | 4K |
| RAG | F | Too heavy | 5.6 tok/s | 63057 ms | 4K |
How Nemotron Nano 9B v2 (9B params) fits at each quantization level on GTX 1660 Super 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 3.5 GB | Low | A83 |
Q3_K_S | 3 | 4.4 GB | Low | F0 |
NVFP4 | 4 | 5.0 GB | Medium | F0 |
Q4_K_M | 4 | 5.5 GB | Medium | F0 |
Q5_K_M | 5 | 6.5 GB | High | F0 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
アップグレードオプション
RTX 3060 12GBコスパ重視Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$329 MSRP
RTX 5060 Ti 16GBコスパ最良Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$449 MSRP
RTX 4060 Ti 16GBNVIDIAアップグレードMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$499 MSRP
No, Nemotron Nano 9B v2 requires more memory than GTX 1660 Super 6GB provides.
Nemotron Nano 9B v2 (9B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Nemotron Nano 9B v2 is Q4_K_M, which balances quality and memory efficiency.
On GTX 1660 Super 6GB, Nemotron Nano 9B v2 achieves approximately 9.1 tokens per second decode speed with a time-to-first-token of 21213ms using Q4_K_M quantization.
For coding workloads, Nemotron Nano 9B v2 on GTX 1660 Super 6GB receives a F grade with 9.1 tok/s and 4K context.
On GTX 1660 Super 6GB, Nemotron Nano 9B v2 can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nemotron-nano-9b-v2-on-gtx-1660-super-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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